OpenTelemetry vs ELK - Choosing the Right Observability Stack

In today’s complex and distributed software architectures, observability has become crucial in maintaining system reliability and performance. Two popular observability stacks—OpenTelemetry and ELK (Elasticsearch, Logstash, and Kibana)—offer robust monitoring, tracing, and logging solutions. But how do you choose the right one for your organization?

In this article, we’ll compare OpenTelemetry and the ELK stack, discussing their features, use cases, and which one might fit depending on your observability needs.

What are OpenTelemetry and ELK Stack?

OpenTelemetry is an open-source observability framework that provides a standardized way to collect and export telemetry data. It aims to create a vendor-neutral approach to instrumentation, allowing developers to instrument their code once and send data to any backend of their choice.

The ELK Stack, on the other hand, is a collection of three open-source projects: Elasticsearch, Logstash, and Kibana. Together, they form a powerful logging and analytics platform. Elasticsearch is the search and analytics engine, Logstash handles data processing, and Kibana provides visualization capabilities.

Key differences:

• Purpose: OpenTelemetry focuses on data collection and exportation, while ELK Stack provides a full-stack solution for data storage, processing, and visualization.
• Scope: OpenTelemetry covers traces, metrics, and logs, whereas ELK Stack primarily excels in log management (though it has expanded to include other data types).
• Vendor neutrality: OpenTelemetry is designed to be vendor-agnostic, while ELK Stack is a specific product suite from Elastic.

Understanding Observability in Modern Systems

Observability has become a cornerstone in managing the reliability and performance of modern, distributed systems. As applications and services become more intricate, and spread across cloud environments and microservices architectures, the ability to monitor, understand, and react to their behavior is critical. Here, we delve deeper into the concept of observability, exploring its importance through the three pillars—logs, metrics, and traces—and examining how tools like OpenTelemetry and the ELK Stack facilitate effective observability strategies.

Importance of Observability

1. Quick Identification and Diagnosis of Issues

In complex systems, failures can arise from numerous, often unexpected interactions between components. Quick identification of these issues is paramount to maintaining system integrity and performance. For example, if an e-commerce website experiences a slowdown during a checkout process, observability tools can help pinpoint whether the issue is related to a backend service, network latency, or a database bottleneck.

2. Insights into System Performance and Behavior

Observability allows teams to gain deep insights into the health and efficiency of their systems. For instance, an operations team might monitor metrics like CPU usage, memory consumption, and I/O operations to predict potential failures or bottlenecks before they impact users.

3. Proactive Monitoring and Alerting

By setting thresholds on metrics or patterns in logs, observability tools can trigger alerts to notify teams about anomalies before they escalate into more significant problems. For example, if the response time for a service exceeds a predefined limit, an alert can be sent out, allowing teams to address the issue proactively.

The Three Pillars of Observability

1. Logs

Logs provide a detailed, timestamped record of events that occur within a system. They are invaluable for debugging and post-mortem analysis. For instance, an application might log every HTTP request it receives, including details like request time, headers, payload, and response. This data can be used to trace the source of errors or unexpected behavior in the system.

2. Metrics

Metrics are numerical data that represent different aspects of the system state over a defined interval. They are crucial for performance monitoring and trend analysis. Metrics might include the number of requests per second, latency, and error rates. For example, monitoring the memory usage metric can help detect memory leaks in an application.

3. Traces

Traces represent the journey of a request as it travels through the various services of a distributed system. Each component of the system adds its data to the trace, allowing developers to see a detailed path of the request and understand interdependencies and bottlenecks. For example, a trace might show that a request made from a mobile app traveled through an API gateway, hit multiple microservices, and required queries to several databases.

Observability with OpenTelemetry and ELK Stack

OpenTelemetry

OpenTelemetry offers a holistic approach to observability by providing tools to instrument applications easily and collect all three types of telemetry data—logs, metrics, and traces. This unified model helps developers and operators correlate data across different dimensions of system performance. For instance, a latency issue captured in a trace can be correlated with a spike in error rates logged simultaneously and an increase in CPU usage metrics.

ELK Stack

While the ELK Stack started primarily as a log management solution, it has evolved to include more observability functions. Elasticsearch is a powerful full-text search engine, Logstash processes incoming data, and Kibana provides visualization capabilities. For enhanced observability, the ELK Stack can be integrated with Elastic APM to collect traces and metrics, giving a more comprehensive view of system performance. For example, logs collected in Elasticsearch can be analyzed to discover patterns, while Kibana can visualize these patterns over time to detect anomalies.

OpenTelemetry: A Deep Dive

OpenTelemetry is an ambitious, community-driven observability framework designed to provide standardized instrumentation, enabling consistent and rich observability signals across distributed systems. It's comprised of three primary components—API, SDK, and Collector—each playing a crucial role in the telemetry data lifecycle. In this detailed exploration, we'll delve into each component, the supported data types, and the instrumentation approaches provided by OpenTelemetry.

Core Components of OpenTelemetry

1. API

The OpenTelemetry API is the foundational layer that defines the contracts for telemetry data creation. It specifies how applications should generate and handle telemetry data like traces, metrics, and logs. This API is crucial because it ensures that any telemetry data you collect is consistent and can be processed by the OpenTelemetry SDK or other compatible tools, regardless of the backend system used for data storage and analysis.

2. SDK

The Software Development Kit (SDK) implements the API and provides additional functionality that is not part of the basic interface. This includes sophisticated data processing capabilities, such as batching, throttling, and filtering of telemetry data before it's exported to backend systems. The SDK is also responsible for managing context propagation in distributed tracing and integrating with the underlying language's runtime to collect system and application performance metrics automatically.

3. Collector

The OpenTelemetry Collector acts as an intermediary that receives, processes, and exports telemetry data. It is a standalone service that can be deployed alongside application services or as a separate component within your infrastructure. The collector supports multiple receiver configurations, allowing it to accept data from various sources, process or transform this data as needed, and then export it to one or more observability backends. The collector's role is vital for scalability and flexibility in complex environments, as it decouples data production from data storage and analysis.

Examples for OpenTelemetry

1. Instrumenting a Python Application Using OpenTelemetry:

For a comprehensive understanding of using OpenTelemetry with Python, this overview from SigNoz provides valuable insights into OpenTelemetry's capabilities and practical setup steps tailored for Python applications. It explains essential components, and installation processes, and demonstrates how to instrument your Python code efficiently, making it a helpful guide alongside the examples provided here.

pip install opentelemetry-api opentelemetry-sdk opentelemetry-instrumentation-flask

from flask import Flask
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter

app = Flask(__name__)

# Setup OpenTelemetry tracing
provider = TracerProvider()
processor = BatchSpanProcessor(ConsoleSpanExporter())
provider.add_span_processor(processor)

FlaskInstrumentor().instrument_app(app)

@app.route("/")
def home():
    return "Hello, OpenTelemetry!"

if __name__ == "__main__":
    app.run(debug=True)

2. OpenTelemetry Collector Configuration Example:

receivers:
  otlp:
    protocols:
      grpc:
      http:

exporters:
  logging:
    loglevel: debug

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [logging]

Supported Programming Languages

OpenTelemetry offers wide language support, with libraries available for Java, Python, Go, JavaScript, and more. This inclusivity enables developers to integrate observability into applications regardless of the technology stack. Moreover, OpenTelemetry's libraries are designed to integrate seamlessly with popular frameworks and libraries in each language ecosystem, making it easier to instrument web servers, database clients, and other common infrastructure components.

OpenTelemetry Data Types and Instrumentation

Data Types

1. Traces OpenTelemetry's tracking system allows you to follow a request as it moves through various services in a distributed system. Traces are composed of spans, where each span represents a discrete unit of work within a larger request. This capability is crucial for identifying performance bottlenecks and understanding the behavior of complex transactions.
2. Metrics Metrics in OpenTelemetry are designed to provide quantitative information about processes running within your systems. These can be measurements of operational metrics such as CPU and memory usage, or application-specific metrics like queue sizes and the number of active users.
3. Logs Logs provide textual records of events that occur within your system. OpenTelemetry aims to unify log management with traces and metrics, providing a cohesive view of observability data. The integration of logs with traces allows developers to correlate log entries with the corresponding trace data, enriching the debugging process.

Instrumentation Approaches

1. Auto-instrumentation OpenTelemetry provides automatic instrumentation capabilities for many popular libraries and frameworks. This means developers can benefit from out-of-the-box traces and metrics without modifying their existing codebase. Auto-instrumentation is a powerful tool for teams looking to quickly enhance their observability with minimal effort.
2. Manual Instrumentation For scenarios where custom data collection is needed, or where auto-instrumentation may not cover specific use cases, OpenTelemetry allows developers to manually integrate telemetry data collection into their applications. This method offers greater control over which parts of an application are instrumented and how telemetry data is reported.

ELK Stack: Complete Solution for Logging and Analytics

In software development and system operations, keeping track of logs and analyzing them is essential to monitor, troubleshoot, and understand how systems work. The ELK Stack—made up of Elasticsearch, Logstash, and Kibana—is a powerful combination that helps manage, analyze, and visualize data in real-time. Let’s take a closer look at each component and how they work together to create a strong logging and analytics platform.

Breaking Down the Components of the ELK Stack

1. Elasticsearch

Elasticsearch is the core of the ELK Stack. It’s a fast and scalable search engine built on Apache Lucene. Elasticsearch quickly finds and analyzes large amounts of data, which is crucial for exploring logs and data almost instantly. It organizes and stores data in a way that makes it easy to search and analyze, whether it's text, numbers, or other types of content. Its ability to handle massive amounts of data and still be fast makes it a reliable tool for large-scale operations.

2. Logstash

Logstash is the data processor in the ELK Stack. It takes data from different sources, processes and transforms it, and then sends it to Elasticsearch. Logstash is very flexible because it can clean, organize, and combine data from different formats. It supports various inputs (where the data comes from), filters (how the data is modified), and outputs (where the data goes). Whether it's logs, metrics, or events, Logstash can handle it all, making sure the data is ready for analysis.

3. Kibana

Kibana is the visualization tool in the ELK Stack. It provides a simple interface for exploring and analyzing data stored in Elasticsearch. With Kibana, you can create live, dynamic dashboards that give you insights into trends, patterns, and problems in your data. You can build charts, graphs, and maps, and easily customize your dashboards to display the information that matters most. Kibana helps you turn raw data into meaningful visuals that support better decision-making.

Examples of ELK Stack

1. Setting up Elasticsearch, Logstash, and Kibana (ELK) with Docker:

version: '3'
services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:7.10.0
    container_name: elasticsearch
    environment:
      - discovery.type=single-node
    ports:
      - "9200:9200"

  logstash:
    image: docker.elastic.co/logstash/logstash:7.10.0
    container_name: logstash
    volumes:
      - ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
    ports:
      - "5000:5000"

  kibana:
    image: docker.elastic.co/kibana/kibana:7.10.0
    container_name: kibana
    ports:
      - "5601:5601"
    depends_on:
      - elasticsearch

2. Basic Logstash Configuration Example:

Create a logstash.conf file to configure Logstash:

input {
  beats {
    port => "5044"
  }
}

filter {
  grok {
    match => { "message" => "%{COMBINEDAPACHELOG}" }
  }
  date {
    match => [ "timestamp" , "dd/MMM/yyyy:HH:mm:ss Z" ]
  }
}

output {
  elasticsearch {
    hosts => "elasticsearch:9200"
  }
  stdout { codec => rubydebug }
}

This example demonstrates how to ingest data from Beats, process logs, and store them in Elasticsearch.

ELK Stack Extensions and Ecosystem

In addition to Elasticsearch, Logstash, and Kibana, there’s another tool that makes data collection easier: Beats. Beats are lightweight data collectors installed on servers to send data directly to Elasticsearch or Logstash. Each type of Beat is designed for a specific task:

• Filebeat: Collects and forwards log data. It watches log files and sends the data to Elasticsearch or Logstash for processing.
• Metricbeat: Collects system and service metrics, such as CPU usage or memory, and forwards them for monitoring.

By using Beats, you can easily gather operational data and send it into your ELK Stack for analysis.

Use Cases for the ELK Stack

The ELK Stack is versatile and can be used in many different ways:

• Log Analysis and Management: Combine logs from multiple systems and analyze them in real time.
• Security and Threat Detection (SIEM): Use ELK for security monitoring and detecting threats.
• Performance Monitoring: Keep track of how your application and system are performing, and quickly identify any issues.
• Business Insights: Analyze business data to understand customer behavior, market trends, and improve operations.

Comparing OpenTelemetry and ELK: Key Considerations

OpenTelemetry and the ELK Stack serve different purposes in observability but complement each other well. OpenTelemetry provides a unified framework to collect traces, metrics, and logs, focusing on seamless integration and reduced manual setup with its automatic instrumentation. Its lightweight design minimizes performance impact, making it highly scalable and suitable for both small applications and large, distributed systems. Supported by major tech companies, OpenTelemetry has a growing ecosystem with regular updates and numerous integrations. On the other hand, the ELK Stack, known for its powerful log management capabilities, is primarily log-centric and requires additional tools, like Beats and APM agents, to handle metrics and traces. Although resource-intensive, ELK excels at handling large data volumes and real-time analytics. With a mature community and extensive plugins, it provides powerful search and visualization options but involves a steeper learning curve due to its multi-component setup.

For more details on setting up OpenTelemetry with Python, see this SigNoz guide, which offers step-by-step insights on installation and integration tailored for Python applications.

Choosing between OpenTelemetry and ELK Stack depends on your organization’s specific needs and priorities.

• If you’re looking for a unified, lightweight solution that provides traces, metrics, and logs right out of the box, OpenTelemetry is the way to go.
• If your focus is primarily on log management and powerful visualizations, and you don’t mind a more complex setup process, ELK Stack may be the better choice.

Both tools offer scalability and customization, but OpenTelemetry is better suited for modern, distributed applications with a need for quick implementation, while ELK excels at handling large datasets and providing deep log analysis with advanced search and visualization features.

Use Cases: When to Choose OpenTelemetry or ELK

Choosing between OpenTelemetry and the ELK Stack depends on your specific observability goals, the nature of your application, and the infrastructure you're managing. Here’s a breakdown of when each tool might be the best fit:

When to Choose OpenTelemetry

OpenTelemetry is particularly advantageous in scenarios where:

• Vendor-Neutral Instrumentation: If you're seeking a standardized, vendor-neutral observability solution that can be used across different cloud providers or on-premises environments, OpenTelemetry is ideal. Its open-source nature ensures that your monitoring setup isn’t locked into any one vendor, allowing flexibility in your infrastructure choices.
• Unified Data Collection for Traces, Metrics, and Logs: OpenTelemetry shines when you need a single framework to handle traces, metrics, and logs. This unified approach simplifies observability by ensuring all three pillars are captured with the same tooling and configuration, making it easier to correlate logs with traces and metrics for a full picture of your system’s performance.
• Building New Applications: When developing a new application, especially one that’s microservices-based, OpenTelemetry helps you future-proof your observability strategy. By incorporating OpenTelemetry from the start, you ensure that your monitoring can scale with your application as it grows, and you won't need to re-engineer your observability setup down the line.
• Distributed Systems and Microservices: OpenTelemetry’s strength lies in its ability to trace requests as they flow through multiple services in a distributed system. This is crucial for understanding complex inter-service communication and pinpointing bottlenecks across a microservices architecture.
• Need for Auto-Instrumentation: OpenTelemetry provides auto-instrumentation libraries for many popular languages, making it easier to collect observability data with minimal manual configuration. This is especially useful for teams who want to set up monitoring quickly without diving deep into custom instrumentation.

When to Choose ELK Stack

The ELK Stack is best suited for environments where:

• Log Management and Analysis: If log management is your top priority, ELK Stack offers a proven solution. With its ability to collect, store, process, and visualize logs, ELK is a comprehensive logging platform. You can centralize logs from multiple sources, making it easy to search, filter, and analyze log data in real time.
• Full-Text Search Capabilities: One of the ELK Stack’s standout features is its powerful full-text search capabilities through Elasticsearch. If you need to quickly sift through large volumes of logs or documents to find specific patterns, errors, or keywords, ELK’s query language allows for deep, fast searches across massive datasets.
• Complete Data Pipeline (Ingestion, Processing, Storage, and Visualization): ELK Stack provides everything you need in a single package—from data ingestion (via Logstash and Beats), to processing and storage (with Elasticsearch), to visualization (using Kibana). This makes it an all-in-one solution, particularly useful if you don’t want to integrate multiple tools for these tasks.
• Security and Compliance: ELK Stack is frequently used in security operations for threat detection, security event management, and compliance monitoring. Its ability to centralize logs and offer granular searches makes it a go-to solution for SIEM (Security Information and Event Management) use cases.
• Complex Log Queries and Reporting: If your primary need is to create custom log reports, dashboards, and visualizations for real-time monitoring, Kibana offers an intuitive interface that allows you to do just that. This is ideal for operations teams who need detailed insights into system behavior, application errors, or security issues.

Combining OpenTelemetry with ELK Stack

In many cases, you don't need to choose between OpenTelemetry and ELK Stack—you can combine the strengths of both for a comprehensive observability solution:

• Use OpenTelemetry for Data Collection: OpenTelemetry can handle the instrumentation and collection of traces, metrics, and logs from your application. Its vendor-neutral framework ensures that you can gather this data efficiently without being tied to a specific monitoring vendor.
• Export Data to ELK for Storage and Analysis: Once OpenTelemetry has collected the observability data, you can export it to Elasticsearch for storage, search, and analysis. ELK’s powerful capabilities for processing and visualizing logs and metrics can enhance the way you analyze your application’s health and performance. Kibana’s real-time dashboards provide deep insights, allowing teams to make data-driven decisions.

This combination offers the best of both worlds—flexibility and standardization from OpenTelemetry for data collection, and robust analytics from ELK Stack for making sense of that data.

Setting Up a Modern Observability Stack with SigNoz

While OpenTelemetry and ELK Stack are powerful solutions, there's another option worth considering: SigNoz. SigNoz is an open-source alternative that combines the best of both worlds:

• It uses OpenTelemetry for data collection, ensuring vendor neutrality and broad language support.
• It provides a full-stack solution similar to ELK, with built-in storage, processing, and visualization capabilities.

Benefits of using SigNoz with OpenTelemetry include:

1. Native support for OpenTelemetry data models.
2. A user-friendly interface for exploring traces, metrics, and logs.
3. Advanced querying and visualization capabilities.
4. Lower resource requirements compared to a full ELK Stack deployment.

SigNoz cloud is the easiest way to run SigNoz. Sign up for a free account and get 30 days of unlimited access to all features.

You can also install and self-host SigNoz yourself since it is open-source. With 19,000+ GitHub stars, open-source SigNoz is loved by developers. Find the instructions to self-host SigNoz.

Compared to ELK Stack and other solutions, SigNoz offers:

• Easier setup and maintenance
• Better performance for high-cardinality data
• More cost-effective scaling for large deployments
• Tighter integration between traces, metrics, and logs

Future Trends in Observability

As the field of observability evolves, we can expect to see:

1. Convergence of logging, metrics, and tracing: Tools will increasingly provide unified platforms for all telemetry data types.
2. AI and machine learning integration: Observability platforms will incorporate more advanced analytics capabilities, such as automated root cause analysis and predictive alerting.
3. Increased adoption of open standards: OpenTelemetry is likely to become the de facto standard for instrumentation, with more vendors offering native support.
4. Evolution of ELK Stack: Elastic will continue to expand the capabilities of their stack, potentially offering tighter integration with OpenTelemetry and other emerging standards.

Key Takeaways

• OpenTelemetry provides a standardized, vendor-neutral approach to instrumentation, ideal for future-proofing your observability strategy.
• ELK Stack offers a mature, full-featured logging and analytics solution with powerful search and visualization capabilities.
• Your choice depends on specific use cases, existing infrastructure, and team expertise.
• Combining approaches — such as using OpenTelemetry for data collection and ELK Stack for analysis — can yield comprehensive observability coverage.
• Consider alternatives like SigNoz that offer the benefits of both OpenTelemetry and ELK Stack in a single, integrated platform.

FAQs

What are the main differences between OpenTelemetry and ELK Stack?

OpenTelemetry focuses on data collection and instrumentation, supporting traces, metrics, and logs in a vendor-neutral way. ELK Stack is a full observability solution, excelling in log management and analysis, with additional capabilities for metrics and traces through extensions.

Can OpenTelemetry and ELK Stack be used together?

Yes, you can use OpenTelemetry for data collection and instrumentation, then export the data to Elasticsearch for storage and analysis using Kibana. This combination leverages the strengths of both systems.

How does OpenTelemetry compare to Elastic APM?

OpenTelemetry is a vendor-neutral framework for instrumentation, while Elastic APM is a specific APM solution tied to the Elastic ecosystem. OpenTelemetry offers more flexibility in terms of backend choice, while Elastic APM provides tighter integration with other Elastic products.

Is OpenTelemetry suitable for small-scale applications?

Yes, OpenTelemetry can be used for applications of any size. Its auto-instrumentation capabilities make it easy to add observability to small applications, and its lightweight design ensures minimal overhead.